Image super resolution reconstruction method based on maximum linear block neighborhood embedding

Abstract

The invention discloses an image super resolution reconstruction method based on maximum linear block neighborhood embedding. The method mainly comprises steps: a training sample set is constructed, a hierarchical division clustering method is adopted for clustering, a nonlinear manifold is approximately divided into multiple maximum linear blocks, and after clustering, medium and high frequency features are used for constructing the maximum linear blocks; a low resolution test image is classified to be divided into edge blocks and non edge blocks, and by adopting two different neighborhood selection modes, the reconstruction result is more accurate; neighborhood selection is carried out; neighborhood embedding is carried out; and image reconstruction is carried out, de-blurring is carried out on the initial reconstructed image, and a complete and clear high resolution reconstructed image is obtained. The maximum linear blocks are approximately obtained from the nonlinear manifold of the training samples through the clustering method, local linear neighborhood embedding is realized in combination with feature representation and neighborhood selection, more accurate high frequency information is reconstructed, the time complexity is greatly reduced, super resolution reconstruction on a natural image is realized, and clearer edge details can be recovered.

Description

[0001] technology neighborhood [0002] The invention belongs to the field of image processing technology, and further relates to an image super-resolution reconstruction method, specifically an image super-resolution reconstruction method based on maximum linear block neighborhood embedding, which can be used for super-resolution reconstruction of natural image data. It is easier to classify, identify and further apply the reconstructed images. Background technique [0003] With the rapid development of electronic information technology, digital image acquisition technology is widely used in many practical applications such as computer vision, remote sensing and medical imaging, video surveillance and so on. In various neighborhoods, with the development of technical equipment and people's needs, there are increasingly higher requirements for the resolution of digital images. However, due to certain limitations in the physical imaging system and imaging environment, high-reso...

AI Technical Summary

Problems solved by technology

[0010] 1. It does not have sufficient discrimination ability, which will lead to the problem of blurred images;

[0011] Second, it is often not easy to obtain a sufficient number of low-resolution images in practical applications, and the registration operation between multiple fuzzy and noisy images is very difficult, so it is not suitable for engineering applications in actual scenes;

[0012] 3. Factors such as inappropriate training samples or feature extraction will lead to obvious artificial traces and noise

Images